Background: Globally, unpaid carers face economic and societal pressures. Unpaid carers’ support is valued at £132 billion a year in the United Kingdom (UK) alone. However, this care comes at a high cost for the carers themselves. Carers providing round the clock care are more than twice as likely to be in bad health than non-carers. These carers are therefore proportionately more likely to need statutory services such as health care provision. It is critical that carers are better supported to be involved in the shaping, delivery and evaluation of the services they receive. Unfortunately, qualitative evidence on how carer organisations can do this better is scarce. Methods: Working collaboratively with a community-based carers organization, we undertook a qualitative study. Purposive sampling was used to recruit 23 participants. Online, semi-structured, one-to-one interviews were conducted with carers, community organization staff and stakeholders to ascertain their experience and views on the involvement service. Results: Firstly, there are a range of benefits resulting from the involvement service. The carers see the service as an opportunity to connect with other carers and share their views and ideas. Secondly, staff and service providers also reported how involvement gave a platform for carers and was of value in helping them shape needs-led services. Thirdly, we found that barriers to good involvement include the lack of a clearly understood, shared definition of involvement as well as the lack of a diverse pool of carer representatives available for involvement activities. Conclusion: The findings from our study provide important insights into how carers, staff and service stakeholders view barriers and enablers to good involvement. The findings will be of interest to a range of community-based organizations interested in further involving members of their community in shaping the services they receive.

With the rapid development of modern AI painting, Chinese university fine arts education is facing numerous challenges and opportunities. This paper analyzes the impact of modern AI painting on traditional art creation and its implications for student skill development. Additionally, it explores the key areas where Chinese university fine arts education needs to transform, including curriculum, teaching methods, and teacher training, while proposing corresponding strategies.

This study analyzes the role of innovation in the development of smart cities in Latin America. It focuses on how emerging technologies and sustainable strategies are being integrated into urban planning and urban development. In this sense, this study seeks to contribute to the smart city literature by answering the following research questions: (i) To what extent smart city innovative initiatives have been addressed in Latin America? and (ii) To what extent scholars have addressed sustainable innovation strategies in the smart city literature? To this end, this is the first comprehensive bibliometric analysis of smart city research in Latin America, with a structured and systematized review of the available literature. This methodological approach allows cluster visualization and detailed analysis of inter-node relationships using the VOSViewer software. The research comprises 4 stages: (a) search criteria; (b) selection of documents; (c) software and data extraction; and (d) analysis of results and trends. Results indicate that studies on the Latin America region began to develop in 2012, with Brazil as a leader in this field and the tourism sector as the most relevant. Nevertheless, strong international collaboration was identified in co-authoring studies, underscoring a cooperative approach to solving common urban problems. The most active research area is technological innovation and sustainability, with focus on solutions for urban mobility, quality of life and smart governance. Finally, this work underlines the need to continue exploring the integration of technology in urban development, suggesting an agenda to guide future research to evaluate the sustainability and long-term impacts of smart city initiatives in Latin America. From the policy perspective, smart city initiatives need to be human-centered to boost smart solutions adoption and to guarantee long term local impacts.

This research presents a novel approach utilizing a self-enhanced chimp optimization algorithm (COA) for feature selection in crowdfunding success prediction models, which offers significant improvements over existing methods. By focusing on reducing feature redundancy and improving prediction accuracy, this study introduces an innovative technique that enhances the efficiency of machine learning models used in crowdfunding. The results from this study could have a meaningful impact on how crowdfunding campaigns are designed and evaluated, offering new strategies for creators and investors to increase the likelihood of campaign success in a rapidly evolving digital funding landscape.